To get a defining advantage over the competitors the banks, as well as other financial institutions must dig deep into technology breakthroughs. Luckily, Artificial Intelligence and Machine Learning are already here to help them to achieve it. Improving data processing will lead to better strategies and fraud prevention levels.
Artificial Intelligence in Banking Statistics
- According to a forecast by the research company Autonomous Next, banks around the world will be able to reduce costs by 22% by 2030 through using artificial intelligence technologies. Savings could reach $1 trillion.
- Financial companies employ 60% of all professionals who have the skills to create AI systems.
- It is expected that face recognition technology will be used in the banking sector to prevent credit card fraud. Face recognition technology will increase its annual revenue growth rate by over 20% in 2020.
How Artificial Intelligence is Used in Banking
The data that banks receive from their customers, investors, partners, and contractors is dynamic and can be used for different purposes, depending on which parameters are used to analyze them. Basically, the scope of AI for banking can be grouped into four large groups.
Improving Customer Experience
When banks and other financial organizations got the opportunity to learn everything about a user and his behavior on a network, they simultaneously gained the opportunity to improve the user experience as much as possible.
For example, if a user has difficulty working with a website or application, chatbots are used to lead him along the right path and at the same time reduce bank support staff’s workload. In addition, modern chatbots can perform simple operations such as locking and unlocking cards as well as send notifications to the user if he has exceeded the overdraft limit — or vice versa if the account balance is higher than usual.
Having a variety of information about user behavior allows financial companies to find out what customers want at the moment, and moreover what they are willing and able to pay for. So, for example, if a client was looking at ads from car dealers, then it might make sense to develop a personalized loan offer — of course, after analyzing his solvency and all possible risks.
Modern AI systems working with big data in banking can not only analyze, but also can make assumptions. For example, in a number of cases, it is possible to predict the intentions of the client if he wants to refuse the services of a banking organization. The knowledge of this intention signals that it is necessary to take additional retention measures, create even more targeted and personalized offers, and as a result, improve the customer experience.
Machine Learning for Safe Bank Transactions
The main advantage of machine learning for the financial sector in the context of fraud prevention is that systems are constantly learning. In other words, the same fraudulent idea will not work twice. This works great for credit card fraud detection in the banking industry.
How Artificial Intelligence Makes Banking Safe
Most financial transactions are made when the user pays for purchases on the Internet or at brick-and-mortar businesses. This means that most fraudulent transactions also occur under the pretext of buying something. AI in banking provides an opportunity to prevent this from happening. For example:
- Cameras with face recognition can determine whether a credit card is in the hands of the rightful owner when buying at a physical point of sale.
- Tracking suspicious IP addresses from which a financial transaction occurs may help prevent fraud with discount coupons as well as identify fraudulent intentions. For example, if someone buys a product in order to return a fake one in its place.
Market Research and Prediction
Machine learning in conjunction with big data can not only collect information but also find specific patterns. For example, it is possible to foresee currency fluctuations, determine the most profitable ideas for investing, level credit risks (and also find a middle ground between the lowest risks and the most suitable loan for a specific user), study competitors, and identify security weaknesses.
Machine Learning allows financial organizations to identify weaknesses in processes and organize the work of full-time employees more efficiently. The simplest example is chatbots, which can successfully cope with advising clients on simple and standard issues. Chatbots also don’t require payment for their work! Besides the fact that working with ML allows companies to reduce costs, it is logical that it also helps increase profits due to improved customer service.
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